AIMC Topic: Child

Clear Filters Showing 3011 to 3020 of 3433 articles

Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center study.

Journal of tropical pediatrics
OBJECTIVE: This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19).

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and provide insights into the extent and nature of the disparities in the ED setting.

A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.

European journal of dermatology : EJD
Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The reactivation rate of LCH exceeds 30%. However, an effective prediction model to predict reactivatio...

Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients.

Radiology
Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation of updated growth standards. Purpose To train an AI model using standing slot-scanning radiographs ...

Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

Human brain mapping
While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains ...

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.

JAMA pediatrics
IMPORTANCE: Acute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple neural networks have been developed to recognize the presence of AOM with limited clinical applicati...

[Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: A deep learning-based method for evaluating the quality of pediatric pelvic X-ray images is proposed to construct a diagnostic model and verify its clinical feasibility.

[Application of convolutional neural networks for the classification of metaphase chromosomes].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics
OBJECTIVE: To train a deep convolutional neural networks (CNN) using a labeled data set to classify the metaphase chromosomes and test its accuracy for chromosomal identification.